2011 QueryBasedLearninginMultiAgentS

• (Sencer, 2011) ⇒ Safiye Sencer. (2011). “Query Based Learning in Multi-Agent Systems.”

Subject Headings: Query-Based Learning System; Multi-Agent Query-Based Learning System.

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Query+Based+Learning+in+Multi-Agent+Systems

Quotes

Author Keywords

• Multi-agent system; Query based learning; Model driven approach; Entropy

Abstract

This study focuses on query based learning in multiagent systems which include both data management operations and coordination activities. The study is oriented on agent based and database systems with model driven approach (MDA) which provides arrangement of data within a multi-agent system by letting filter with query based learning which supports the decision mechanism within the system. It uses the similarity measure on maximum entropy approach, which is often to find out interesting and meaningful patterns from databases. At the same time, it may generate a variety of rules, such as classification rules, throughout to learning rules of the query based learning process.

3. Agent Based Query Learning System

Query based learning is a part of machine learning which optimizes the performance criterion using current and past situation data. The model defined up to some parameters, and learning is the execution and optimizes the parameters of the model using the past experience. The [suggested model able descriptive to gain knowledge from data, derivative to obtain rule from knowledge base with query then predict future decisions. When interface realizes the query process, consider the following requirements. In order to identify and use the characteristics relevant to the task to be taken into outline in the interface level components are: information and resources; control parameters and activations. Information and resources includes detail information about task, cases, attributes with name, goal, index and hierarchy framework. The dynamic knowledge base presents with input and output interface. Control parameters aim to check and detect some failures in the system with temporal constraints, error toleration and functional limits. During the realization of the query based learning, system realizes some activation such as querying, reasoning, collaborating, planning and acting (Fig. 2).

Figure 2. Interaction of learning activity.

==== 3.1 Knowledge Processing ====

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 QueryBasedLearninginMultiAgentS	Safiye Sencer			Query Based Learning in Multi-Agent Systems